GMS location: 902

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.275 0.402 1.682 NaN NaN
forest winter 2016 0.989 0.000e+00 0.221 0.352 1.446 0.502 5.814
baseline winter 2017 0.958 0.091 0.335 0.424 2.427 NaN NaN
forest winter 2017 0.975 0.091 0.269 0.373 1.910 0.481 5.199
baseline winter 2018 0.980 0.103 0.327 0.433 2.002 NaN NaN
forest winter 2018 0.993 0.103 0.279 0.382 2.130 0.496 4.919
baseline winter 2019 0.974 0.071 0.389 0.408 3.668 NaN NaN
forest winter 2019 0.987 0.071 0.294 0.346 3.631 0.487 4.504
baseline all 0.975 0.077 0.328 0.416 3.668 NaN NaN
forest all 0.987 0.077 0.264 0.362 3.631 0.492 5.141

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.275 0.402 1.682 NaN NaN
elr winter 2016 0.979 0.000e+00 0.261 0.392 1.370 0.581 5.663
baseline winter 2017 0.958 0.091 0.335 0.424 2.427 NaN NaN
elr winter 2017 0.983 0.121 0.282 0.384 2.186 0.531 4.898
baseline winter 2018 0.980 0.103 0.327 0.433 2.002 NaN NaN
elr winter 2018 0.993 0.138 0.291 0.398 2.115 0.560 5.202
baseline winter 2019 0.974 0.071 0.389 0.408 3.668 NaN NaN
elr winter 2019 0.987 0.071 0.344 0.395 4.096 0.550 5.702
baseline all 0.975 0.077 0.328 0.416 3.668 NaN NaN
elr all 0.985 0.099 0.293 0.392 4.096 0.557 5.389

Extended logistic regression plots

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